Gyula Kovács

According to our database, Gyula Kovács authored at least 11 papers between 1999 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2023

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs.

Carla den Ouden, Andong Zhou, Vinay Mepani, Gyula Kovács, Rufin Vogels, Daniel Feuerriegel

NeuroImage, October, 2023

2020

Getting to Know Someone: Familiarity, Person Recognition, and Identification in the Human Brain.

Gyula Kovács

J. Cogn. Neurosci., 2020

2017

Causal evidence of the involvement of the right occipital face area in face-identity acquisition.

Géza Gergely Ambrus, Fabienne Windel, A. Mike Burton, Gyula Kovács

NeuroImage, 2017

2016

Causal evidence of the involvement of the number form area in the visual detection of numbers and letters.

Mareike Grotheer, Géza Gergely Ambrus, Gyula Kovács

NeuroImage, 2016

2015

Applications of the inverse infection problem on bank transaction networks.

András Bóta, András Csernenszky, Lajos Gyõrffy, Gyula Kovács, Miklós Krész, András Pluhár

Central Eur. J. Oper. Res., 2015

2014

Repetition probability effects for inverted faces.

Mareike Grotheer, Petra Hermann, Zoltán Vidnyánszky, Gyula Kovács

NeuroImage, 2014

2012

Stimulus repetition probability effects on repetition suppression are position invariant for faces.

Gyula Kovács, Lara Iffland, Zoltán Vidnyánszky, Mark W. Greenlee

NeuroImage, 2012

2010

Neural correlates of stimulus-invariant decisions about motion in depth.

Gyula Kovács, Csaba Cziraki, Mark W. Greenlee

NeuroImage, 2010

Neural Correlates of Generic versus Gender-specific Face Adaptation.

Nadine Kloth, Stefan R. Schweinberger, Gyula Kovács

J. Cogn. Neurosci., 2010

2008

Position-specific and position-invariant face aftereffects reflect the adaptation of different cortical areas.

Gyula Kovács, Csaba Cziraki, Zoltán Vidnyánszky, Stefan R. Schweinberger, Mark W. Greenlee

NeuroImage, 2008

1999

Winner-Take-All Network Utilising Pseudoinverse Reconstruction Subnets Demonstrates Robustness on the Handprinted Character Recognition Problem.

J. Körmendy-Rácz, S. Szabó, J. Lörincz, G. Antal, Gyula Kovács, András Lörincz

Neural Comput. Appl., 1999